Categorizing financial transactions based on spending patterns

ABSTRACT

During operation of a system, a financial transaction of an individual is associated with one or more predefined categories based on scores that indicate the likelihood of association. For example, a given predefined category may include a merchant name (such as the name of a potential counterparty in the financial transaction) and/or an attribute associated with one or more merchants. The score for a given predefined category may be determined based on a number of occurrences of the given predefined category in financial-transaction histories of a subset of a group of individuals who have common spending patterns in their financial-transaction histories. Moreover, the spending pattern may be based on values of financial transactions in the financial-transaction histories.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is related to U.S. Non-provisional patent applicationSer. No. 14/031,800, entitled “Categorizing Financial Transactions Basedon Business Preferences,” by Ilya A. Izrailevsky and Sony Joseph filedon 19 Sep. 2013; to U.S. Non-provisional patent application Ser. No.14/031,811, entitled “Categorizing Financial Transactions Based onSpending Preferences,” by Ilya A. Izrailevsky and Sony Joseph, filed on19 Sep. 2013; and to U.S. Non-provisional patent application Ser. No.14/031,821, entitled “Categorizing Financial Transactions Based onLocation Preferences,” by Sony Joseph and Ilya A. Izrailevsky, filed on19 Sep. 2013, the contents of each of which are herein incorporated byreference.

BACKGROUND

The present disclosure generally relates to computer-based techniquesfor categorizing financial transactions. More specifically, the presentdisclosure relates to a computer-based technique for categorizingfinancial transactions based on customer spending-pattern cohorts.

A wide variety of features in financial software can be enabled byassigning a financial transaction of a user to a predefined category(which is referred to as ‘categorizing’ the financial transaction). Forexample, by automatically categorizing the financial transaction for theuser, manual entry of this information can be avoided. Moreover, oncethe financial transaction has been assigned to a predefined category, itmay be easier to generate graphical and tabular summaries of the user'sfinancial activity. In addition, information about categorized financialtransactions can be leveraged by third parties (such as banks orfinancial institutions) that provide financial services, such astargeting of financial services to particular individuals based on thecategorized financial transactions. Consequently, categorized financialtransactions can improve the user experience when using the financialsoftware and/or may comprise valuable information for the third parties.

However, it is often difficult to categorize financial transactions. Forexample, the information associated with a financial transaction (suchas information included on a credit- or debit-card receipt) often doesnot uniquely specify a predefined category. Instead, a fragment orabbreviated name of a counterparty in the financial transaction maymatch several predefined categories. This ambiguity may degrade theusefulness of categorizing financial transactions.

SUMMARY

The disclosed embodiments relate to a computer system that categorizes afinancial transaction. During operation, the computer system receivesinformation specifying the financial transaction for an individual.Then, the computer system determines one or more scores indicating alikelihood that the financial transaction is associated with one or morepredefined categories based on numbers of occurrences of the one or morepredefined categories in financial-transaction histories of a subset ofa group of individuals. Note that the individual is included in thesubset, the subset includes two or more individuals having a commonspending pattern in the financial-transaction histories, and thespending pattern is based on values of financial transactions in thefinancial-transaction histories.

Moreover, the information may identify a potential counterparty in thefinancial transaction. More specifically, determining the one or morescores may involve comparing the counterparty to a set of predefinedcategories to identify the one or more predefined categories, where agiven predefined category includes: a merchant name and/or an attributeassociated with one or more merchants. Additionally, a score of thegiven predefined category may correspond to a number of occurrences ofthe given predefined category in the financial-transaction histories. Insome embodiments, a score of the given predefined category is normalizedby the numbers of occurrences of the one or more predefined categoriesin the financial-transaction histories.

Note that the information may include a value of the financialtransaction. Moreover, the value of the financial transaction may beused to determine the subset based on the common spending pattern.

In some embodiments, the computer system assigns the individual to thegroup based on an analysis of the financial-transaction histories of theindividual and of the group. Furthermore, assigning the individual tothe group of individuals may include: identifying multiple groups ofindividuals by performing a clustering analysis of financialtransactions in the financial-transaction histories of the individualsin the multiple groups of individuals using an unsupervised-learningtechnique; determining distance values between the financialtransactions in the financial-transaction histories of the individualand the financial transactions in the financial-transaction histories ofthe multiple groups of individuals; and assigning the individual to thegroup of individuals based on the determined distance values.

Another embodiment provides a method that includes at least some of theoperations performed by the computer system.

Another embodiment provides a computer-program product for use with thecomputer system. This computer-program product includes instructions forat least some of the operations performed by the computer system.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 is a flow chart illustrating a method for categorizing afinancial transaction in accordance with an embodiment of the presentdisclosure.

FIG. 2 is a flow chart illustrating the method of FIG. 1 in accordancewith an embodiment of the present disclosure.

FIG. 3 is a flow chart illustrating a method for categorizing afinancial transaction in accordance with an embodiment of the presentdisclosure.

FIG. 4 is a flow chart illustrating the method of FIG. 3 in accordancewith an embodiment of the present disclosure.

FIG. 5 is a flow chart illustrating a method for categorizing afinancial transaction in accordance with an embodiment of the presentdisclosure.

FIG. 6 is a flow chart illustrating the method of FIG. 5 in accordancewith an embodiment of the present disclosure.

FIG. 7 is a flow chart illustrating a method for categorizing afinancial transaction in accordance with an embodiment of the presentdisclosure.

FIG. 8 is a flow chart illustrating the method of FIG. 7 in accordancewith an embodiment of the present disclosure.

FIG. 9 is a block diagram illustrating a system that performs themethods of FIGS. 1-8 in accordance with an embodiment of the presentdisclosure.

FIG. 10 is a block diagram illustrating a computer system that performsthe methods of FIGS. 1-8 in accordance with an embodiment of the presentdisclosure.

Note that like reference numerals refer to corresponding partsthroughout the drawings. Moreover, multiple instances of the same partare designated by a common prefix separated from an instance number by adash.

DETAILED DESCRIPTION

Embodiments of a computer system, a technique for categorizing afinancial transaction, and a computer-program product (e.g., software)for use with the computer system are described. During this financialtechnique, a financial transaction of an individual is associated withone or more predefined categories based on scores that indicate thelikelihood of association. For example, a given predefined category mayinclude a merchant name (such as the name of a potential counterparty inthe financial transaction) and/or an attribute associated with one ormore merchants. The score for a given predefined category may bedetermined based on a number of occurrences of the given predefinedcategory in financial-transaction histories of a subset of a group ofindividuals who have common spending patterns in theirfinancial-transaction histories. Moreover, the spending pattern may bebased on values of financial transactions in the financial-transactionhistories.

By categorizing the financial transaction, the financial technique mayreduce ambiguity and improve the accuracy of the determined one or morepredefined categories. This capability may reduce or eliminate the needfor manual entry of one or more categories associated with the financialtransaction by the individual, which, in turn, may facilitate graphicaland tabular summaries of the individual's financial activity. Inaddition, the categorized financial transaction may facilitate improvedfinancial services, such as targeting of financial services to theindividual. Consequently, the financial technique may improve the userexperience when using financial software, may reduce actual andopportunity costs by facilitating improved targeting, and/or may promotecommercial activity.

In the discussion that follows, a user may include: an individual or aperson (for example, an existing customer, a new customer, a serviceprovider, a vendor, a contractor, etc.), an organization, a businessand/or a government agency. Furthermore, a ‘business’ should beunderstood to include: for-profit corporations, non-profit corporations,organizations, groups of individuals, sole proprietorships, governmentagencies, partnerships, etc. Additionally, a financial transaction mayinvolve a product or a service (such as medical care) that is paid forusing a type of currency, an asset and/or by barter. The financialtransaction may be conducted by an individual and/or a business.

We now describe embodiments of the financial technique. FIG. 1 presentsa flow chart illustrating a method 100 for categorizing a financialtransaction, which may be performed by a computer system (such ascomputer system 1000 in FIG. 10). During operation, the computer systemreceives information specifying the financial transaction for anindividual (operation 110). For example, the information may include areceipt for the financial transaction, which may include: the date andtime of the financial transaction, the location of the financialtransaction, the amount or value of the financial transaction, and atleast a fragment or portion of a merchant or counterparty's name in thefinancial transaction.

Then, the computer system determines one or more scores indicating alikelihood that the financial transaction is associated with one or morepredefined categories based on numbers of occurrences of the one or morepredefined categories in financial-transaction histories of a subset ofa group of individuals (operation 114). Note that the individual isincluded in the subset, and the subset includes two or more individualshaving a common spending pattern in the financial-transaction histories(i.e., the group may be subdivided based on spending patterns of theindividuals, so that similar individuals are clustered in subsets of thegroup, thereby facilitating comparisons of individuals with similarbehaviors). This spending pattern may be based on values of financialtransactions in the financial-transaction histories (e.g., the subsetmay include individuals who spend similar amounts of money in financialtransactions).

As noted previously, the information may identify a potentialcounterparty in the financial transaction. Furthermore, determining theone or more scores (operation 114) may involve comparing thecounterparty to a set of predefined categories (e.g., by comparingcharacters) to identify the one or more predefined categories, and agiven predefined category includes: a merchant name and/or an attributeassociated with one or more merchants (such as a type of business, e.g.,restaurants). Additionally, a score of the given predefined categorycorresponds to a number of occurrences of the given predefined categoryin the financial-transaction histories. For example, the score may belarger if the given predefined category occurs more often in thefinancial-transaction histories than other predefined categories. Insome embodiments, a score of the given predefined category is normalizedby the numbers of occurrences of the one or more predefined categoriesin the financial-transaction histories (e.g., the maximum score may be anumber between zero and one).

Moreover, as noted previously, the information may include a value ofthe financial transaction. The value of the financial transaction may beused to determine the relevant subset to use when determining the one ormore scores based on the common spending pattern.

In some embodiments, the computer system optionally assigns theindividual to the group based on an analysis of thefinancial-transaction histories of the individual and of the group(operation 112). Furthermore, assigning the individual to the group ofindividuals may include: identifying multiple groups of individuals byperforming a clustering analysis of financial transactions in thefinancial-transaction histories of the individuals in the multiplegroups of individuals using an unsupervised-learning technique;determining distance values between the financial transactions in thefinancial-transaction histories of the individual and the financialtransactions in the financial-transaction histories of the multiplegroups of individuals; and assigning the individual to the group ofindividuals based on the determined distance values.

In an exemplary embodiment, the financial technique is implemented usingone or more electronic devices (such as a computer or a portableelectronic device, e.g., a cellular telephone) and one or more servers(such as a computer or a computer system), which communicate through anetwork, such as a cellular-telephone network and/or the Internet. Thisis illustrated in FIG. 2, which presents a flow chart illustratingmethod 100 (FIG. 1).

During the method, a user of electronic device 210-1 (such as theindividual) may conduct a financial transaction (operation 216) with acounterparty. The financial transaction may be conducted electronicallyvia electronic device 210-1. (However, in other embodiments theindividual conducts the financial transaction in person.) For example,the user may conduct the financial transaction using: cash, a check, acredit card, a debit card and, more generally, a financial vehicle.Server 212, which is associated with the counterparty or a financialinstitution that provides the financial vehicle, may provide theinformation (operation 218) specifying the financial transaction toserver 214 (which is associated with a provider of the financialtechnique).

After receiving the information (operation 222), server 214 maydetermine the one or more scores (operation 224) indicating thelikelihood that the financial transaction is associated with the one ormore predefined categories based on the numbers of occurrences of theone or more predefined categories in financial-transaction histories ofa subset of a group of individuals.

Then, server 214 may optionally provide the one or more scores(operation 226) and the associated predefined categories to a thirdparty (not shown), such as one or more banks and, more generally, one ormore financial institutions. This third party may compensate theprovider of the financial technique for the categorization of thefinancial transaction.

In some embodiments, prior to receiving the information (operation 222),server 214 optionally assigns the individual to the group (operation220) based on an analysis of the financial-transaction histories of theindividual and of the group.

In an exemplary embodiment, the financial technique enablescategorization, in which financial transactions are associated withpredefined categories. In addition to functional benefits (such asgraphical and tabular summaries of the individuals' financialactivities), this capability can significantly improve the customerexperience when using financial software, such as income-tax softwareand/or accounting software.

In contrast with existing approaches that compare the name similarity ofmerchants, the location of a financial transactions, and keywords thatoccur in descriptions of the financial transactions to infer thecategory associated with the financial transaction, in the financialtechnique improved categorization (with enhanced coverage and accuracy)is obtained using data mining, as well as other financial-transactionsignal identification and processing techniques. In particular, one ormore unique aspects of the data associated with the financialtransactions (such as inferred cohort preferences) are used todisambiguate the predefined categories associated with the financialtransactions.

For example, in the financial technique cohorts or segments of users mayinitially be identified based on the financial-spending behavior of theusers. These user-segment cohorts or groups may be identified based on anumber of parameters, which may include: the merchants the usersvisited, when the users visited the merchants, the average amount spentby the users, and/or the frequency of the users' spending. In someembodiments, additional user demographic data is used to augment theuser-segment cohorts.

Then, the merchant-to-category mapping of the users within auser-segment cohort is aggregated and normalized for that user-segmentcohort. These maps may be used to create implicit community rules, asopposed to explicit ones that the users specify. For example, asdescribed further below, the mapping may be based on the spendingpatterns of the users (and, more generally, user behavior patterns).Note that, if one user in the user-segment cohort has a financialtransaction that cannot be associated with one or more predefinedcategories by direct comparison of the information specifying thefinancial transaction with the one or more predefined categories, thenthe categorization data (such as the maps) from the other users in thesame user-segment cohort may be used to categorize the financialtransaction.

Thus, by leveraging user-segment cohorts (that are identified based onsimilar behavior patterns) to extract the category preferences of theseuser-segment cohorts, financial transactions that could not previouslybe categorized can be disambiguated.

As an illustration of the financial technique, a user-segment cohort maybe identified for users who frequent fast-food restaurants at least fivetimes a month, and who spend, on average, $20-$30. Most of the users inthis user-segment cohort may have previous financial transactions inwhich the merchant ‘Costco’ is assigned or associated with a predefinedcategory entitled ‘food.’

Subsequently, one of the users in the user-segment cohort may have afinancial transaction with ‘Costco’ that could not be categorized basedon the information specifying the financial transaction. By using thefinancial-transaction histories of the user-segment cohort, anassociation with the predefined category entitled ‘food’ may bedetermined for this financial transaction, as opposed to the predefinedcategories of ‘electronics’ or ‘grocery.’

In general, when disambiguating the categorization of a financialtransaction, the spending-pattern score of a user u in a user-segmentcohort to a predefined category c may be calculated asSpend-Pattern Score(u,c)=TF(c in cohort categories(u))·IDF(c),withTF(c in cohort categories(u))=(term frequency(c in cohortcategories(u))),^(1/2) andIDF(c)=1+log(N/(N(c)+1)),where cohort categories(u) includes the categories in the user-segmentcohort, N is the number of users in the user-segment cohort, and N(c) isthe number of users in the user-segment cohort that have used predefinedcategory c at least once. The spending-pattern score is proportional tothe number of times the predefined category c appears in theuser-segment cohort of user u, but is offset by the frequency of thepredefined category across all the users in the user-segment cohort.This helps to control for the fact that some predefined categories aretoo generic or are more common than others.

In an exemplary embodiment, there is a financial transaction that cannotbe accurately categorized based on the merchant name. For example,‘Costco’ may be difficult to categorize because they sell a wide varietyof products. In this case, a user-segment cohort may be used tocategorize the financial transaction by generating a probability scorethat a given predefined category is associated with the financialtransaction. Thus, a financial transaction with a merchant identified as‘Alxfd’ in the information may be associated with ‘Alexander steakhouse’and ‘Alexander electronics.’ If the user-segment cohort prefers upscalerestaurants with an average bill greater than $400, then ‘Alexandersteakhouse’ is a more-likely predefined category. The financialtechnique may be used to determine a normalized score or weight for eachpredefined category or for those predefined categories thatapproximately match ‘Alxfd’ (such as those with Levenshtein distancesless than a threshold, e.g., a difference of three characters).

More generally, the users in the user-segment cohort may be binned orsubdivided based on their spending. Thus, users that spend more than afirst amount, on average, per financial transaction and less than asecond amount, on average, per financial transaction may be clustered ina subset. Subsequently, when categorizing a financial transaction havingan associated amount or value between the first amount and the secondamount, the financial transactions in a financial-transaction history ofthe users in the subset may be used to determine the one or more scores.

In general, a wide variety of factors may be used in determining the oneor more scores. Moreover, the score for a given predefined category maybe a weighted summation of the scores or weights for the differentfactors. In the discussion that follows, embodiments of the financialtechnique that use several different factors are described. Theresulting scores or weights for these factors may be used independentlyor in conjunction with each other to determine the associations of thepredefined categories and financial transactions.

In another embodiment, a business-size preference of an individual (asopposed to the group or the subset of the group) is used to categorizethe financial transaction in the financial technique. This embodiment ofthe financial technique is based on the fact that some individualsprefer local merchants to national chains. Thus, if 75% of the financialtransactions in the financial-transaction history of an individual arewith local merchants, and there are two possible predefined categoriesassociated with a financial transaction (one local and one a nationalchain), the scores for these two predefined categories are,respectively, 0.75 and 0.25. Similarly, if an individual has apreference for a particular predefined category in the financialtransactions in the financial-transaction history of the individual,this preference may be used to determine the scores of the predefinedcategories.

This financial technique is shown in FIG. 3, which presents a flow chartillustrating a method 300 for categorizing a financial transaction Thismethod may be performed by a computer system (such as computer system1000 in FIG. 10). During operation, the computer system receivesinformation specifying the financial transaction for an individual(operation 110). Then, the computer system determines one or more scoresindicating a likelihood that the financial transaction is associatedwith one or more predefined categories based on financial-transactionpreferences of the individual (operation 310), where thefinancial-transaction preferences are specified by afinancial-transaction history of the individual.

As in method 100 (FIGS. 1 and 2), the information may identify apotential counterparty in the financial transaction. Moreover,determining the one or more scores (operation 310) may involve comparingthe counterparty to a set of predefined categories (e.g., by comparingcharacters) to identify the one or more predefined categories, where agiven predefined category includes: a merchant name and/or an attributeassociated with one or more merchants (such as a type of business, e.g.,restaurants).

Alternatively or additionally, the information may specify a type of thecounterparty, where the one or more scores are based on a number ofoccurrences of the type of the counterparty in the financial-transactionhistory. A score of the given predefined category may be normalized bythe numbers of occurrences of the one or more predefined categories inthe financial-transaction history (so that the scores are between zeroand one). Furthermore, the type of the counterparty may include: abusiness having one or more establishments in a geographic area, and/ora business having multiple establishments in a second geographic areathat is larger than the geographic area and that includes the firstgeographic area. Thus, the business may be local or part of a nationalchain. In this way, if the individual has a preference for ‘local’businesses in their financial-transaction history, this may be used todetermine the scores of the predefined categories when there isambiguity.

In some embodiments, the one or more scores are based on numbers ofoccurrences of the one or more predefined categories in thefinancial-transaction history. Moreover, a score of the given predefinedcategory may be normalized by the numbers of occurrences of the one ormore predefined categories in the financial-transaction history. In thisway, if the individual has a preference for one or more predefinedcategories in their financial-transaction history, this may be used todetermine the scores of the predefined categories when there isambiguity. For example, the scores may, at least in part, be determinedby the frequencies of occurrences of the predefined categories in thefinancial-transaction history.

In an exemplary embodiment, the financial technique is implemented usingone or more electronic devices (such as a computer or a portableelectronic device, e.g., a cellular telephone) and one or more servers(such as a computer or a computer system), which communicate through anetwork, such as a cellular-telephone network and/or the Internet. Thisis illustrated in FIG. 4, which presents a flow chart illustratingmethod 300 (FIG. 3).

During the method, the user of electronic device 210-1 (such as theindividual) may conduct the financial transaction (operation 216) withthe counterparty. The financial transaction may be conductedelectronically via electronic device 210-1. Server 212, which isassociated with the counterparty or the financial institution thatprovides a financial vehicle, may convey the information (operation 218)specifying the financial transaction to server 214 (which is associatedwith the provider of the financial technique).

After receiving the information (operation 410), server 214 maydetermine the one or more scores (operation 412) indicating thelikelihood that the financial transaction is associated with the one ormore predefined categories based on the financial-transactionpreferences of the individual.

Then, server 214 may optionally provide the one or more scores(operation 414) and the associated predefined categories to the thirdparty (not shown).

In an exemplary embodiment, the financial technique identifies abusiness-size and/or a predefined-category preference of the user. Thispreference may be based on the user's customers and vendors, asspecified in their financial-transaction history.

For example, the size of a business may be determined by parameters suchas the number of employees at the business and its annual revenue. Thisbusiness information may be determined or inferred by accessing abusiness-listings database. The business sizes may be normalized acrossother businesses in the same area (or geographic region) and/or otherbusinesses with which the user conducts business.

Subsequently, if the user conducts a financial transaction that cannotbe categorized, the user's business-size preference may be used tocategorize the financial transaction. For example, if there are twopossible predefined categories associated with the financial transaction(such as those that approximately match the counterparty name in thefinancial transaction), and the user has a strong preference for localbusinesses or small businesses in their financial-transaction history,this preference may be used to determine the relative weights of the twopossible predefined categories.

Thus, the business-size preference of a user may be identified as 10employees and $200,000 annual revenue based on the previous financialtransactions in their financial-transaction history. This preference mayindicate that the user tends to shop at local stores and restaurants (asopposed to large chains). If a subsequent financial transaction isassociated with the merchant name ‘Coupa,’ which cannot be categorizedbased on the existing information associated with the financialtransaction, the identified business-size preference may be used toorient and disambiguate the predefined category more toward a predefinedcategory of ‘Coffee Shops’ instead of a predefined category of ‘SoftwareDesign.’ This is because the predefined category of ‘Coupa Café,’ amerchant with $150,000 in annual revenue, is chosen over the predefinedcategory of ‘Coupa Software,’ a merchant with $4,500,000 in annualrevenue.

Alternatively or additionally, the category preference of the user maybe determined using the user's financial-transaction history. Forexample, the category preference may be determined by analyzing thenumber of times a particular predefined category appears in the user'sfinancial-transaction history. This category preference may benormalized across those of other users in the same area (or geographicregion) and/or based on the other predefined categories with which theuser conducts business.

Subsequently, if the user conducts a financial transaction that cannotbe categorized, the user's category preference may be used to categorizethe financial transaction. For example, if there are two possiblepredefined categories associated with the financial transaction (such asthose that approximately match the counterparty name in the financialtransaction), and the user has a strong preference for a particularpredefined category in their financial-transaction history, thispreference may be used to determine the relative weights of the twopossible predefined categories.

Thus, the category preference of a user may be identified as ‘Bakeries,’as opposed to ‘Fast-Food Restaurants,’ based on the previous financialtransactions in their financial-transaction history. This preference mayindicate that the user tends to go to bakeries for breakfast, as opposedto fast-food establishments. If a subsequent financial transaction isassociated with the merchant name ‘Soda Inc.,’ which cannot becategorized based on the existing information associated with thefinancial transaction, the identified category preference may be used toorient and disambiguate the predefined category more toward a predefinedcategory of ‘Bakeries,’ instead of ‘Fast-Food Restaurants.’ This isbecause ‘Soda Inc.’ occurs as both a bakery and a fast-food restaurantin the predefined categories.

Note that the score of a user u to a business b based on thebusiness-size preference for disambiguating financial-transactioncategories may be calculated asScore(u,b)=NormalizedAcrossUsersSize(u,b)·NormalizedAcrossBusinessesSize(u,b),withNormalizedAcrossUsersSize(u,b)=log(1+S(b)/S _(avg)(u)); andNormalizedAcrossBusinessesSize(u,b)=S(b)/S _(avg),where S(b) equals the size/number of employees and annual revenue ofbusiness b, S_(avg)(u) equals the average size/number of employees andannual revenue of businesses with which user u conducts business, andS_(avg) equals the average size/number of employees and annual revenueat all known businesses in the area (e.g. within Xmiles). In thisanalysis, the normalized business size is normalized across allbusinesses in the area, as well as across the financial-transactionhistory of the user u.

Similarly, the score of user u to a predefined category c based on theircategory preference(s) for disambiguating financial-transactioncategories may be calculated ascore(u,c)=TF(c in category(u))·/DF(category(c)),withTF(c in category(u))=(term frequency(c in category(u)))^(1/2), andIDF(c)=1+log(N/(N(c)+1)),where category(u) equals predefined categories from thefinancial-transaction history of user u, term frequency(c incategory(u)) equals the number of times predefined category c occurs inthe financial-transaction categories of user u, N equals the totalnumber of users in the area (e.g. within Xmiles), and N(c) equals thenumber of users in the area who have used predefined category c at leastonce. In this analysis, the score is proportional to the number of timesthe predefined category c appears in the financial-transaction historyof user u, but is offset by the frequency of the predefined categoryacross all users. This helps to control for the fact that somepredefined categories are too generic or are more common than others.

In another embodiment, an individual's spending volume or preference(s)(i.e., the amount the individual spends, on average, at a given merchantor for a given predefined category) is used to categorize the financialtransaction in the financial technique. This is shown in FIG. 5, whichpresents a flow chart illustrating a method 500 for categorizing afinancial transaction. This method may be performed by a computer system(such as computer system 1000 in FIG. 10). During operation, thecomputer system receives information specifying the financialtransaction for an individual (operation 110), where the informationincludes a value or amount associated with the financial transaction.Then, the computer system determines one or more scores indicating alikelihood that the financial transaction is associated with one or morepredefined categories based on the value and spending preferences of theindividual (operation 510), where the spending preferences are specifiedby a financial-transaction history of the individual.

As in methods 100 (FIGS. 1 and 2) and 300 (FIGS. 3 and 4), theinformation may identify a potential counterparty in the financialtransaction. Moreover, determining the one or more scores (operation510) may involve comparing the counterparty to a set of predefinedcategories (e.g., by comparing characters) to identify the one or morepredefined categories, where a given predefined category includes: amerchant name and/or an attribute associated with one or more merchants(such as a type of business, e.g., restaurants).

Alternatively or additionally, the spending preferences may includevalues associated with the financial-transaction history. For example,the one or more scores may be Bayesian probabilities in which the apriori probabilities are based on the spending preferences in theindividual's financial-transaction history.

Furthermore, the one or more scores may be determined based on aposition of the value in a distribution of the values associated withthe financial-transaction history. For example, if the value associatedwith the financial transaction is well above the mean spendingpreference(s) for the individual, predefined categories associated withluxury merchants may more likely be associated with the financialtransaction than predefined categories associated with low-costmerchants.

In an exemplary embodiment, the financial technique is implemented usingone or more electronic devices (such as a computer or a portableelectronic device, e.g., a cellular telephone) and one or more servers(such as a computer or a computer system), which communicate through anetwork, such as a cellular-telephone network and/or the Internet. Thisis illustrated in FIG. 6, which presents a flow chart illustratingmethod 500 (FIG. 5).

During the method, the user of electronic device 210-1 (such as theindividual) may conduct the financial transaction (operation 216) withthe counterparty. The financial transaction may be conductedelectronically via electronic device 210-1. Server 212, which isassociated with the counterparty or the financial institution thatprovides a financial vehicle, may convey the information (operation 218)specifying the financial transaction to server 214 (which is associatedwith the provider of the financial technique).

After receiving the information (operation 610), server 214 maydetermine the one or more scores (operation 612) indicating thelikelihood that the financial transaction is associated with the one ormore predefined categories based on the spending preferences of theindividual.

Then, server 214 may optionally provide the one or more scores(operation 614) and the associated predefined categories to the thirdparty (not shown).

In an exemplary embodiment, the financial technique identifies thespending preferences of the user based on their financial-transactionhistory. In particular, the spending preference may be determined bycalculating the amount of money the user spends while shopping or goingout to a restaurant (and, more generally, in financial transactions witha merchant). Then, the average amount of money spent by all users whovisit this merchant is determined. The spending preference may benormalized across other businesses in the same area (or geographicregion) and other businesses with which the user conducts business.

Subsequently, if the user conducts a financial transaction that cannotbe categorized, the user's spending preferences may be used tocategorize the financial transaction. For example, if there are twopossible predefined categories associated with the financial transaction(such as those that approximately match the counterparty name in thefinancial transaction), and if the value or amount associated with afinancial transaction exceeds a threshold in their financial-transactionhistory, the user has a spending preference for a particular predefinedcategory. This spending preference may be used to determine the relativeweights of the two possible predefined categories based on the value ofthe financial transaction.

Thus, a user's spending preference, on average, may be identified as$8.50 per financial transaction based on the previous financialtransactions in their financial-transaction history. Consequently, itmay be more likely that the user tends to eat at fast-food restaurantsthan more expensive restaurants. If the user subsequently conducts afinancial transaction with the merchant name ‘Burger’ that cannot becategorized based on the information associated with the financialtransaction, the spending preference can be used to orient anddisambiguate the predefined category more toward the predefined categoryof ‘fast-food restaurants’ than the predefined category of ‘full-servicerestaurants.’

Note that the score based on the spending preference of a user u to abusiness b for disambiguating financial-transaction categories may becalculated asSpend(u,b)=NormalizedAcrossUsersSpend(u,b)·NormalizedAcrossBusinessesSpend(u,b),withNormalizedAcrossUsersSpend(u,b)=log(1+S(u,b)/S _(avg)(b), andNormalizedAcrossBusinessesSpend(u,b)=S(u,b)/S(u),where S(u, b) equals the spend volume/money spent by user u at businessb, S_(avg)(b) equals the average spend volume/money spent by all userswho visited business b, and S(u) equals the spend volume/money spent byuser u across all businesses with which they conduct business. In thisanalysis, the normalized spending preference normalizes users for thatbusiness, as well as for the user across all businesses.

In another embodiment, the location preferences of an individual and thelocation of a financial transaction are used to categorize the financialtransaction in the financial technique. This is shown in FIG. 7, whichpresents a flow chart illustrating a method 700 for categorizing afinancial transaction. This method may be performed by a computer system(such as computer system 1000 in FIG. 10). During operation, thecomputer system receives information specifying the financialtransaction for an individual (operation 110), where the informationincludes a location associated with the financial transaction. Then, thecomputer system determines one or more scores indicating a likelihoodthat the financial transaction is associated with one or more predefinedcategories based on the location associated with the financialtransaction and one or more location preferences of the individual(operation 710), where the one or more location preferences arespecified by a financial-transaction history of the individual.

As in methods 100 (FIGS. 1 and 2), 300 (FIGS. 3 and 4) and 500 (FIGS. 5and 6), the information may identify a potential counterparty in thefinancial transaction. Moreover, determining the one or more scores(operation 710) may involve comparing the counterparty to a set ofpredefined categories (e.g., by comparing characters) to identify theone or more predefined categories, where a given predefined categoryincludes: a merchant name and/or an attribute associated with one ormore merchants (such as a type of business, e.g., restaurants).

Furthermore, the location preferences may correspond to locationsassociated with the financial transactions in the financial-transactionhistory. Additionally, the one or more scores may be determined bycomparing the location associated with the financial transaction and theone or more location preferences of the individual to identify amatching location preference. In some embodiments, the one or morescores are determined based on numbers of occurrences of the one or morepredefined categories associated with the financial-transactionhistories having the matching location preference.

In an exemplary embodiment, the financial technique is implemented usingone or more electronic devices (such as a computer or a portableelectronic device, e.g., a cellular telephone) and one or more servers(such as a computer or a computer system), which communicate through anetwork, such as a cellular-telephone network and/or the Internet. Thisis illustrated in FIG. 8, which presents a flow chart illustratingmethod 700 (FIG. 7).

During the method, the user of electronic device 210-1 (such as theindividual) may conduct the financial transaction (operation 216) withthe counterparty. The financial transaction may be conductedelectronically via electronic device 210-1. Server 212, which isassociated with the counterparty or the financial institution thatprovides a financial vehicle, may convey the information (operation 218)specifying the financial transaction to server 214 (which is associatedwith the provider of the financial technique).

After receiving the information (operation 810), server 214 maydetermine the one or more scores (operation 812) indicating thelikelihood that the financial transaction is associated with the one ormore predefined categories based on the location preferences of theindividual and the location of the financial transaction.

Then, server 214 may optionally provide the one or more scores(operation 814) and the associated predefined categories to the thirdparty (not shown).

In an exemplary embodiment, the financial technique identifies thelocation preferences of the user based on their financial-transactionhistory. In particular, predefined categories may be identified byclustering these predefined categories around the locations ofassociated financial transactions. For example, a location preferencemay be identified in which the user's spending in San Francisco tends tobe clustered around entertainment and bars, while the same user'sspending in Mountain View tends to be clustered around groceries. Theselocation preferences can then be used to provide probabilistic scoresfor categorization based on location.

Thus, if the user conducts a financial transaction that cannot becategorized, the user's location preference(s) may be used to categorizethe financial transaction. For example, if there are two possiblepredefined categories associated with the financial transaction (such asthose that approximately match the counterparty name in the financialtransaction), and the user has a strong preference for a predefinedcategory when there is a particular location or region in theirfinancial-transaction history, this location preference may be used todetermine the relative weights of the two possible predefined categoriesbased on the location associated with the financial transaction. Inessence, this embodiment of the financial technique leverages the factthat the user's behavioral pattern also drives their spending patterns.

For example, Jane Doe may live in Mountain View and work in SanFrancisco. She does most of her grocery shopping closer to home(especially around the weekdays). Moreover, she generally goes for happyhour after work on Friday. Furthermore, she also heads to the city onweekends, and most of those visits tend to be museums or plays. Usingthis information, cluster of spending rules may be identified based onJane's location preferences.

If a subsequent financial transaction cannot be categorized, but thelocation and time are known (e.g., ‘Jake's Place, Fri 06/1331.0’), thelocation preferences may be used to distinguish between two possiblepredefined categories, the merchants ‘Jake's Pharmacy’ and ‘Jake'sDrinks’ Because these two predefined categories are very similar, it maybe difficult to categorize the financial transaction. However, using thelocation preferences, ‘Jake's Drinks’ can be determined as the likelypredefined category for the financial transaction given that Jane tendsto visit bars on Friday evenings.

Note that a score based on the location preference(s) of a user u to abusiness b for disambiguating financial-transaction categories may becalculated asLocalityOfSpendScore(u,b)=Bayesian(b in locality cluster(u)),Bayesian(b in locality cluster(u))=integral(patternrecognizer(b|u,t)·posterior probability(t,b)·d(t)),pattern recognizer(b|u,t)=p(u,b)·p(b,t)/integral(p(u,b)·p(b,t)·d(b)),and posterior probability(t,b)=p(t)·p(b,t)|p(b),where locality cluster(u) equals a list of businesses from user u'sfinancial-transaction history clustered, t equals the time interval overwhich u's financial-transaction history is integrated, d(t) denotes theintegration of user u's financial-transaction history over time, d(b)denotes the integration over all businesses with which user u conductedbusiness, p(u, b) equals the probability/frequency of occurrence ofbusiness b in user u's financial-transaction history, p(b, t) equals theprobability/frequency of occurrence of business b during time intervalt, p(t) equals the probability/frequency of occurrence of time intervalt, p(u, t) equals the probability/frequency of occurrence of timeinterval t in user u's financial-transaction history, and p(u) equalsthe probability/frequency of occurrence of user u over all other users.In this analysis, score based on location preferences is determinedusing random time intervals. Moreover, in the sequential use of Bayes'formula, when more financial-transaction history data become availableafter calculating a posterior distribution, the posterior becomes thenext prior. The selection of time intervals is determined in conjunctionwith the locality of the user at the specified time interval. This helpsto identify whether the user is traveling and if the user spends moneyin a location during a specific time interval.

In some embodiments of methods 100 (FIGS. 1 and 2), 300 (FIGS. 3 and 4),500 (FIGS. 5 and 6), and 700 (FIGS. 7 and 8), there may be additional orfewer operations. Moreover, the order of the operations may be changed,and/or two or more operations may be combined into a single operation.

We now describe embodiments of a system and the computer system, andtheir use. FIG. 9 presents a block diagram illustrating a system 900that can be used, in part, to perform operations in methods 100 (FIGS. 1and 2), 300 (FIGS. 3 and 4), 500 (FIGS. 5 and 6), and 700 (FIGS. 7 and8). In this system, during the financial technique one or moreelectronic devices 210 may use a software product, such as a softwareapplication that is resident on and that executes on one or moreelectronic devices. (Alternatively, the one or more users may interactwith a web page that is provided by server 212 via network 912, andwhich is rendered by a web browser on the one or more electronic devices210. For example, at least a portion of the software application may bean application tool that is embedded in the web page, and which executesin a virtual environment of the web browser. Thus, the application toolmay be provided to electronic devices 210 via a client-serverarchitecture.) This software application may be a standalone applicationor a portion of another application that is resident on and whichexecutes on the one or more electronic devices 210 (such as a softwareapplication that is provided by server 212 or that is installed andwhich executes on the one or more electronic devices 210). In anexemplary embodiment, the software product may be financial software,such as accounting software, income-tax software or payroll software.

During the financial technique, the user of one of electronic devices210 (such as electronic device 210-1) may use the financial software toconduct the financial transaction with server 212 of a counterparty vianetwork 912. Then, server 212 may convey the information specifying thefinancial transaction to server 214 via network 912.

After receiving the information, server 214 may determine the one ormore scores (operation 812) indicating the likelihood that the financialtransaction is associated with the one or more predefined categories.The one or more scores may be based on: the numbers of occurrences ofthe one or more predefined categories in financial-transaction historiesof a subset of a group of individuals (who have a common spendingpattern as the user) and a value of the financial transaction (which mayspecify the subset); financial-transaction preferences of the user (suchas a business-size preference and/or a predefined-category preference);spending preferences of the user; and/or the location preferences of theuser and the location of the financial transaction.

Then, server 214 may optionally provide the one or more scores(operation 814) and the associated predefined categories to optionalserver 914 (which is associated with third party) via network 912.

Note that information in system 900 may be stored at one or morelocations in system 900 (i.e., locally or remotely). Moreover, becausethis data may be sensitive in nature, it may be encrypted. For example,stored data and/or data communicated via network 912 may be encrypted.

FIG. 10 presents a block diagram illustrating a computer system 1000that performs methods 100 (FIGS. 1 and 2), 300 (FIGS. 3 and 4), 500(FIGS. 5 and 6), and 700 (FIGS. 7 and 8), such as server 214 (FIGS. 2,4, 6, 8 and 9). Computer system 1000 includes one or more processingunits or processors 1010, a communication interface 1012, a userinterface 1014, and one or more signal lines 1022 coupling thesecomponents together. Note that the one or more processors 1010 maysupport parallel processing and/or multi-threaded operation, thecommunication interface 1012 may have a persistent communicationconnection, and the one or more signal lines 1022 may constitute acommunication bus. Moreover, the user interface 1014 may include: adisplay 1016, a keyboard 1018, and/or a pointer 1020, such as a mouse.

Memory 1024 in computer system 1000 may include volatile memory and/ornon-volatile memory. More specifically, memory 1024 may include: ROM,RAM, EPROM, EEPROM, flash memory, one or more smart cards, one or moremagnetic disc storage devices, and/or one or more optical storagedevices. Memory 1024 may store an operating system 1026 that includesprocedures (or a set of instructions) for handling various basic systemservices for performing hardware-dependent tasks. Memory 1024 may alsostore procedures (or a set of instructions) in a communication module1028. These communication procedures may be used for communicating withone or more computers and/or servers, including computers and/or serversthat are remotely located with respect to computer system 1000.

Memory 1024 may also include multiple program modules (or sets ofinstructions), including: transaction module 1030 (or a set ofinstructions), categorizing module 1032 (or a set of instructions),and/or encryption module 1034 (or a set of instructions). Note that oneor more of these program modules (or sets of instructions) mayconstitute a computer-program mechanism.

During the financial technique, transaction module 1030 may collect ormay access data 1036 (via communication module 1028 and communicationinterface 1012) associated with software product 1038 used by users1040. This data may include information associated with financialtransactions 1042.

Then, categorizing module 1032 may determine one or more scores 1044indicating the likelihood that one of financial transactions 1042 isassociated with one or more predefined categories 1046. The one or morescores 1044 may be based on: the numbers of occurrences 1048 of the oneor more predefined categories in financial-transaction histories 1050 ofa subset 1054 of a group 1052 of individuals (who have a common spendingpattern 1056 as one of users 1040) and a value of the financialtransaction (which may specify the subset, and which may be included indata 1036); financial-transaction preferences 1058 of the one of users1040 (such as a business-size preference 1060 and/or apredefined-category preference 1062); spending preferences 1064 of theone of users 1040; and/or location preferences 1066 of the one of users1040 and a location of the financial transaction (which may be includedin data 1036).

Then, categorizing module 1032 may optionally provide (via communicationmodule 1028 and communication interface 1012) the one or more scores1044 and the associated predefined categories to a third party 1068.

In some embodiments, prior to determining the one or more scores 1044,categorizing module 1032 optionally assigns the one of users 1040 togroup 1052 based on an analysis of financial-transaction histories 1050of the one of the users 1040 and group 1052.

Because information used in the financial technique may be sensitive innature, in some embodiments at least some of the data stored in memory1024 and/or at least some of the data communicated using communicationmodule 1028 is encrypted or decrypted using encryption module 1034.

Instructions in the various modules in memory 1024 may be implementedin: a high-level procedural language, an object-oriented programminglanguage, and/or in an assembly or machine language. Note that theprogramming language may be compiled or interpreted, e.g., configurableor configured, to be executed by the one or more processors 1010.

Although computer system 1000 is illustrated as having a number ofdiscrete items, FIG. 10 is intended to be a functional description ofthe various features that may be present in computer system 1000 ratherthan a structural schematic of the embodiments described herein. In someembodiments, some or all of the functionality of computer system 1000may be implemented in one or more application-specific integratedcircuits (ASICs) and/or one or more digital signal processors (DSPs).

Computer system 1000, as well as electronic devices, computers andservers in system 1000, may include one of a variety of devices capableof manipulating computer-readable data or communicating such databetween two or more computing systems over a network, including: apersonal computer, a laptop computer, a tablet computer, a mainframecomputer, a portable electronic device (such as a cellular telephone orPDA), a server, a point-of-sale terminal and/or a client computer (in aclient-server architecture). Moreover, network 912 (FIG. 9) may include:the Internet, World Wide Web (WWW), an intranet, a cellular-telephonenetwork, LAN, WAN, MAN, or a combination of networks, or othertechnology enabling communication between computing systems.

Electronic devices 210 (FIGS. 2, 4, 6, 8 and 9), server 212 (FIGS. 2, 4,6, 8 and 9), server 214 (FIGS. 2, 4, 6, 8 and 9), system 900 (FIG. 9),and/or computer system 1000 may include fewer components or additionalcomponents. Moreover, two or more components may be combined into asingle component, and/or a position of one or more components may bechanged. In some embodiments, the functionality of electronic devices210 (FIGS. 2, 4, 6, 8 and 9), server 212 (FIGS. 2, 4, 6, 8 and 9),server 214 (FIGS. 2, 4, 6, 8 and 9), system 900 (FIG. 9) and/or computersystem 1000 may be implemented more in hardware and less in software, orless in hardware and more in software, as is known in the art.

In the preceding description, we refer to ‘some embodiments.’ Note that‘some embodiments’ describes a subset of all of the possibleembodiments, but does not always specify the same subset of embodiments.

The foregoing description is intended to enable any person skilled inthe art to make and use the disclosure, and is provided in the contextof a particular application and its requirements. Moreover, theforegoing descriptions of embodiments of the present disclosure havebeen presented for purposes of illustration and description only. Theyare not intended to be exhaustive or to limit the present disclosure tothe forms disclosed. Accordingly, many modifications and variations willbe apparent to practitioners skilled in the art, and the generalprinciples defined herein may be applied to other embodiments andapplications without departing from the spirit and scope of the presentdisclosure. Additionally, the discussion of the preceding embodiments isnot intended to limit the present disclosure. Thus, the presentdisclosure is not intended to be limited to the embodiments shown, butis to be accorded the widest scope consistent with the principles andfeatures disclosed herein.

What is claimed is:
 1. A computer-implemented method for identifying acategory of a transaction described by an electronic record, the methodcomprising: receiving the electronic record, wherein informationincluded in the electronic record incompletely describes thetransaction, and wherein the information identifies at least a portionof a name of a counterparty that participated in the transaction;comparing the information to a set of predefined categories to identifyone or more predefined categories; determining that more than onecategory has been identified; disambiguating the category of thetransaction by: assigning a user to a group of individuals including twoor more individuals other then the user based on an analysis oftransaction histories of the two or more individuals other than the userand a transaction history of the user by: identifying multiple groups ofindividuals by performing a clustering analysis of transactions intransaction histories of the individuals in the multiple groups ofindividuals using an unsupervised-learning technique; determiningdistance values between transactions in the transaction history of theuser and the transactions in the transaction histories of theindividuals in the multiple groups of individuals; and assigning theuser to the group of individuals based on the distance values; for eachcategory in the one or more predefined categories, determining a scorefor the category by using a number of occurrences of the category in thetransaction histories of the two or more individuals other than theuser, by using an average spending of the user with the counterparty, byusing an average spending of the two or more individuals with thecounterparty, and by using an overall average spending of the user,wherein the score indicates a likelihood that the transaction isassociated with the category, wherein the two or more individuals have acommon spending pattern with the user that is based on the user and thetwo or more individuals spending more than a first amount, on average,per financial transaction and less than a second amount, on average, perfinancial transaction, wherein determining the score comprisescalculating a product of a first normalized spending and a secondnormalized spending, wherein the first normalized spending comprises alogarithm of the average spending of the user with the counterpartydivided by the average spending of the two or more individuals with thecounterparty, and wherein the second normalized spending comprises aratio of the average spending of the user with the counterparty dividedby the overall average spending of the user; and categorizing thetransaction as belonging to a given category from the one or morepredefined categories based on the score for each category of the one ormore predefined categories; and automatically targeting a service to theuser based on the given category determined through the disambiguating.2. The method of claim 1, wherein each predefined category from the oneor more predefined categories includes one of: a merchant name and anattribute associated with one or more merchants.
 3. The method of claim1, wherein the score for each category of the one or more predefinedcategories is normalized by numbers of occurrences of the one or morepredefined categories in the transaction histories of the two or moreindividuals other than the user.
 4. The method of claim 1, wherein theinformation includes a value of the transaction.
 5. The method of claim4, wherein the value of the transaction is used to identify the two ormore individuals having a common spending pattern with the user.
 6. Anon transitory computer-readable medium comprising instructions that,when executed by a processor, cause the processor perform a method foridentifying a category of a transaction described by an electronicrecord, the method comprising: receiving the electronic record, whereininformation included in the electronic record incompletely describes thetransaction, and wherein the information identifies at least a portionof a name of a counterparty that participated in the transaction;comparing the information to a set of predefined categories to identifyone or more predefined categories; determining that more than onecategory has been identified; disambiguating the category of thetransaction by: assigning a user to a group of individuals including twoor more individuals other then the user based on an analysis oftransaction histories of the two or more individuals other than the userand a transaction history of the user by: identifying multiple groups ofindividuals by performing a clustering analysis of transactions intransaction histories of the individuals in the multiple groups ofindividuals using an unsupervised-learning technique, determiningdistance values between transactions in the transaction history of theuser and the transactions in the transaction histories of theindividuals in the multiple groups of individuals; and assigning theuser to the group of individuals based on the distance values;determining, for each category in the one or more predefined categories,a score for the category by using a number of occurrences of thecategory in the transaction histories of the two or more individualsother than the user by using an average spending of the user with thecounterparty, by using an average spending of the two or moreindividuals with the counterparty, and by using an overall averagespending of the user, wherein the score indicates a likelihood that thetransaction is associated with the category, wherein the two or moreindividuals have a common spending pattern with the user that is basedon the user and the two or more individuals spending more than a firstamount, on average, per financial transaction and less than a secondamount, on average, per financial transaction, wherein determining thescore comprises calculating a product of a first normalized spending anda second normalized spending, wherein the first normalized spendingcomprises a logarithm of the average spending of the user with thecounterparty divided by the average spending of the two or moreindividuals with the counterparty, and wherein the second normalizedspending comprises a ratio of the average spending of the user with thecounterparty divided by the overall average spending of the user; andcategorizing the transaction as belonging to a given category from theone or more predefined categories based on the score for each categoryof the one or more predefined categories; and automatically targeting aservice to the user based on the given category determined through thedisambiguating.
 7. The non-transitory computer-readable medium of claim6, wherein each predefined category from the one or more predefinedcategories includes one of: a merchant name and an attribute associatedwith one or more merchants.
 8. A computer system, comprising: aprocessor; and non-transitory computer-readable medium comprisinginstructions that, when executed by the processor, cause the processorto perform a method for identifying a category of a transactiondescribed by an electronic record, the method comprising: receiving theelectronic record, wherein information included in the electronic recordincompletely describes the transaction, and wherein the informationidentifies at least a portion of a name of a counterparty thatparticipated in the transaction; comparing the information to a set ofpredefined categories to identify one or more predefined categories;determining that more than one category has been identified;disambiguating the category of the transaction by: assigning a user to agroup of individuals including two or more individuals other then theuser based on an analysis of transaction histories of the two or moreindividuals other than the user and a transaction history of the userby: identifying multiple groups of individuals by performing aclustering analysis of transactions in transaction histories of theindividuals in the multiple groups of individuals using anunsupervised-learning technique; determining distance values betweentransactions in the transaction history of the user and the transactionsin the transaction histories of the individuals in the multiple groupsof individuals, and assigning the user to the group of individuals basedon the distance values; determining, for each category in the one ormore predefined categories, a score for the category by using a numberof occurrences of the category in the transaction histories of the twoor more individuals other than the user by using an average spending ofthe user with the counterparty, by using an average spending of the twoor more individuals with the counterparty, and by using an overallaverage spending of the user, wherein the score indicates a likelihoodthat the transaction is associated with the category, wherein the two ormore individuals have a common spending pattern with the user that isbased on the user and the two or more individuals spending more than afirst amount, on average, per financial transaction and less than asecond amount, on average, per financial transaction, whereindetermining the score comprises calculating a product of a firstnormalized spending and a second normalized spending, wherein the firstnormalized spending comprises a logarithm of the average spending of theuser with the counterparty divided by the average spending of the two ormore individuals with the counterparty, and wherein the secondnormalized spending comprises a ratio of the average spending of theuser with the counterparty divided by the overall average spending ofthe user; and categorizing the transaction as belonging to a givencategory from the one or more predefined categories based on the scorefor each category of the one or more predefined categories; andautomatically targeting a service to the user based on the givencategory determined through the disambiguating.
 9. The computer systemof claim 8, wherein each predefined category includes one of: a merchantname and an attribute associated with one or more merchants.
 10. Themethod of claim 1, wherein each category in the predefined categoriescorresponds to a different type of product or service of a plurality oftypes of products and services for transactions in the transactionhistories of the two or more individuals other than the user and atransaction history of the user, and wherein categorizing thetransaction comprises determining a type of product or service from theplurality of types of products and services that the transaction is mostlikely associated with.
 11. The method of claim 1, wherein theinformation comprises an identifier for a merchant for the transaction,wherein determining the score for the category comprises using a numberof times that the two or more individuals have mapped the merchant tothe category for previous transactions in the transaction histories ofthe two or more individuals other than the user, and wherein the givencategory corresponds to a category in the predefined categories that thetwo or more individuals have mapped to the merchant.
 12. The method ofclaim 11, wherein determining the score for the category comprisescalculating the score as a function of a number of individuals in thetwo or more individuals and a number of individuals in the two or moreindividuals that have used the category at least once.
 13. Thenon-transitory computer-readable medium of claim 6, wherein eachcategory in the predefined categories corresponds to a different type ofproduct or service of a plurality of types of products and services fortransactions in the transaction histories of the two or more individualsother than the user and a transaction history of the user, and whereincategorizing the transaction comprises determining a type of product orservice from the plurality of types of products and services that thetransaction is most likely associated with.
 14. The non-transitorycomputer-readable medium of claim 6, wherein the information comprisesan identifier for a merchant for the transaction, wherein determiningthe score for the category comprises using a number of times that thetwo or more individuals have mapped the merchant to the category forprevious transactions in the transaction histories of the two or moreindividuals other than the user, and wherein the given categorycorresponds to a category in the predefined categories that the two ormore individuals have mapped to the merchant.
 15. The non-transitorycomputer-readable medium of claim 6, wherein determining the score forthe category comprises calculating the score as a function of a numberof individuals in the two or more individuals and a number ofindividuals in the two or more individuals that have used the categoryat least once.
 16. The non-transitory computer-readable medium of claim6, wherein a value of the transaction is used to identify the two ormore individuals having a common spending pattern with the user.